Pulsed detection threshold computation module

A pulse detection and calculation module technology, applied in radio wave measurement systems, instruments, etc., can solve the problems of shortened detection distance, reduced probability of interception, and high probability of false alarm of receivers, and achieves the best interception and easy implementation.

Active Publication Date: 2010-11-17

CHINA INFORMATION & ELECTRONICSE DEV HEFEI

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Problems solved by technology

In this process, an excessively high threshold reduces the probability of interception or shortens the d...

Abstract

The invention discloses a pulsed detection threshold computation module, comprising a noise decorrelation module, a noise direct current elimination module, a noise wild value elimination module, a noise mean square root module, a pulsed preprocessing module, a pulsed elimination module, a noise mean value module and a threshold output module which are connected in sequence, wherein a video pulsesignal with noise is respectively input to the noise decorrelation module, the noise direct current elimination module, the pulsed preprocessing module and the pulsed elimination module; the noise decorrelation module, the noise direct current removing module, the noise wild value elimination module and the noise mean square root module are connected in sequence; the noise mean square root moduleis respectively output to the noise wild value elimination module, the pulsed elimination module and the threshold output module; and the pulsed preprocessing module, the pulsed elimination module, the noise mean value module and the threshold output module are connected in sequence. The pulsed detection threshold computation module of the invention can calculate characteristic value mean value and variance at real time, dynamically and high precisely, acquires pulsed detection optimum threshold according to preset false-alarm demand to realize optimum interception of the pulsed signal, and is convenient to be realized in programmable devices.

Application Domain

Wave based measurement systems

Technology Topic

Impulse detectionDC block +7

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[0007] Such as figure 1 As shown, the pulse detection threshold calculation module of the present invention includes an orderly connected noise decorrelation module, a noise removal module, a noise removal module, a noise root mean square module, a pulse preprocessing module, a pulse elimination module, and a noise average Module and threshold output module. The noisy video pulse signal to be detected is input to the noise decorrelation module 1, the pulse preprocessing module 5, and the pulse elimination module 6 respectively. Noise de-correlation module, noise de-DC module 2, noise de-outlier module 3 and noise RMS module 4 are connected in sequence; noise RMS module is output to noise de-outlier module, pulse elimination module and threshold output module 8 respectively; The pulse preprocessing module, the pulse elimination module, the noise average module 7 and the threshold output module are connected in sequence.

[0008] The noise decorrelation module 1 uses time extraction to reasonably eliminate the correlation of noisy video signal sequences. The principle is: the correlation between adjacent points of a random number sequence decreases with the increase of the time interval, and the random number sequence is extracted. When the extraction interval is large enough, it can be considered that the two points after extraction are no longer Relevant, but independent of each other.

[0009] The noise removing DC module 2 uses the differential method to eliminate the DC component in the video noise video signal. The principle is: if a digital sequence contains a DC component, the difference between two adjacent points will remove the DC component.

[0010] The noise removal module 3 uses the noise probability distribution characteristics, and the noise value higher than several times the noise root mean square value can be regarded as an abnormal value and discarded. The principle is: after the random noise sequence containing the signal is subjected to the differential operation, the edge of the signal will generate a short pulse with a large amplitude, which has a very wide frequency spectrum, which increases the energy of the noise, that is, the root mean square value of the noise and the actual situation The comparison will become larger, which is the interference caused by the signal and must be eliminated. After elimination, a random noise signal with a mean value of zero whose noise power is very similar to the actual situation is obtained.

[0011] The noise root mean square module 4 uses an infinite impulse response filter to smoothly filter the noise squared value, and then uses a successive approximation method to obtain the square root value. The principle is: firstly, the output of the noise removal module is squared, and then filtered with an infinite impact low-pass response filter with extremely narrow bandwidth to obtain the mean square value of the noise, and finally the mean square value is squared , Get the root mean square value of noise.

[0012] The pulse preprocessing module 5 uses an infinite impulse response filter to filter the noise pulse signal containing the pulse. The principle is: input the noise sequence containing the signal into an infinite impulse low-pass response filter with extremely narrow bandwidth for filtering. Due to the phase lag characteristic of the infinite impulse response filter, a noise sequence containing a distorted pulse signal is obtained. The pulse rises The edge becomes slower, and its output can be seen as a rough approximation of the noise mean in turn.

[0013] The pulse elimination module 6 uses the output of the pulse preprocessing module plus several times the root mean square output of the noise root mean square module as the decision threshold, and the noise pulse signal larger than this threshold is considered an abnormal value and discarded. The principle is: the distorted pulse signal output by the pulse preprocessing module has a slower rising edge compared with the pulse signal in the original noise. Using this difference and the influence of noise, several times the root mean square output of the module is used as the decision threshold. The noise pulse signal larger than this threshold is regarded as an abnormal value and discarded. In this way, most of the impulse components in the original video signal can be eliminated. The quasi-noise that contains only part of the pulse component is obtained.

[0014] The noise average module 7 uses an infinite impulse response filter to filter the noise signal after the pulse is eliminated. The principle is: use an infinite impact low-pass response filter with a very narrow bandwidth to filter the reserved data output by the pulse elimination module, and the result is the quasi-average value of the noise.

[0015] By cascading the pulse elimination module and the noise average module, the true value of the noise can be approached without limitation. In this embodiment, the pulse elimination module and the noise average module are cascaded twice. If a more accurate approximation is required, a cascade of more than two stages can be used.

[0016] The threshold output module 8 uses the output of the noise average module plus the output of the noise root mean square module as its output. The principle is: the threshold level is the mean value of the noise plus the root mean square value of the noise multiplied by a coefficient, and the value greater than this level is regarded as an impulse signal, otherwise it is regarded as noise.

[0017] The main technical indicators of the present invention are related to the parameter settings, and the following indicators can be achieved in general:

[0018] Root mean square error of noise: ≤±3LSB. Noise mean error: ≤±3LSB.

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Easy to implement

Pulsed detection threshold computation module

AI-Extracted Technical Summary

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Description & Claims & Application Information

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